Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations907
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.2 KiB
Average record size in memory96.1 B

Variable types

Text4
Categorical1
Numeric7

Alerts

Duration_Years is highly overall correlated with LevelHigh correlation
Exchange_Rate is highly overall correlated with Insurance_USDHigh correlation
Insurance_USD is highly overall correlated with Exchange_Rate and 2 other fieldsHigh correlation
Level is highly overall correlated with Duration_YearsHigh correlation
Living_Cost_Index is highly overall correlated with Insurance_USD and 1 other fieldsHigh correlation
Rent_USD is highly overall correlated with Insurance_USD and 2 other fieldsHigh correlation
Tuition_USD is highly overall correlated with Rent_USD and 1 other fieldsHigh correlation
Visa_Fee_USD is highly overall correlated with Tuition_USDHigh correlation
Tuition_USD has 103 (11.4%) zerosZeros

Reproduction

Analysis started2025-10-02 05:28:17.229079
Analysis finished2025-10-02 05:28:29.130303
Duration11.9 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Country
Text

Distinct71
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:29.268992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length12
Mean length6.5104741
Min length2

Characters and Unicode

Total characters5905
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.8%

Sample

1st rowUSA
2nd rowUK
3rd rowCanada
4th rowAustralia
5th rowGermany
ValueCountFrequency (%)
uk93
 
9.7%
australia86
 
8.9%
usa78
 
8.1%
canada76
 
7.9%
germany33
 
3.4%
france27
 
2.8%
south24
 
2.5%
korea23
 
2.4%
netherlands21
 
2.2%
switzerland20
 
2.1%
Other values (67)481
50.0%
2025-10-02T10:58:29.537059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a996
16.9%
n445
 
7.5%
r419
 
7.1%
e382
 
6.5%
i360
 
6.1%
l275
 
4.7%
u236
 
4.0%
d220
 
3.7%
t216
 
3.7%
A203
 
3.4%
Other values (36)2153
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a996
16.9%
n445
 
7.5%
r419
 
7.1%
e382
 
6.5%
i360
 
6.1%
l275
 
4.7%
u236
 
4.0%
d220
 
3.7%
t216
 
3.7%
A203
 
3.4%
Other values (36)2153
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a996
16.9%
n445
 
7.5%
r419
 
7.1%
e382
 
6.5%
i360
 
6.1%
l275
 
4.7%
u236
 
4.0%
d220
 
3.7%
t216
 
3.7%
A203
 
3.4%
Other values (36)2153
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a996
16.9%
n445
 
7.5%
r419
 
7.1%
e382
 
6.5%
i360
 
6.1%
l275
 
4.7%
u236
 
4.0%
d220
 
3.7%
t216
 
3.7%
A203
 
3.4%
Other values (36)2153
36.5%

City
Text

Distinct556
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:29.729419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.567806
Min length2

Characters and Unicode

Total characters6864
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)43.0%

Sample

1st rowCambridge
2nd rowLondon
3rd rowToronto
4th rowMelbourne
5th rowMunich
ValueCountFrequency (%)
singapore18
 
1.8%
london11
 
1.1%
melbourne11
 
1.1%
sydney11
 
1.1%
city10
 
1.0%
san9
 
0.9%
canberra8
 
0.8%
newcastle7
 
0.7%
brisbane7
 
0.7%
coast7
 
0.7%
Other values (585)900
90.1%
2025-10-02T10:58:30.019322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a749
 
10.9%
e590
 
8.6%
n530
 
7.7%
o507
 
7.4%
r457
 
6.7%
i407
 
5.9%
l313
 
4.6%
t299
 
4.4%
s273
 
4.0%
u227
 
3.3%
Other values (52)2512
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)6864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a749
 
10.9%
e590
 
8.6%
n530
 
7.7%
o507
 
7.4%
r457
 
6.7%
i407
 
5.9%
l313
 
4.6%
t299
 
4.4%
s273
 
4.0%
u227
 
3.3%
Other values (52)2512
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a749
 
10.9%
e590
 
8.6%
n530
 
7.7%
o507
 
7.4%
r457
 
6.7%
i407
 
5.9%
l313
 
4.6%
t299
 
4.4%
s273
 
4.0%
u227
 
3.3%
Other values (52)2512
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a749
 
10.9%
e590
 
8.6%
n530
 
7.7%
o507
 
7.4%
r457
 
6.7%
i407
 
5.9%
l313
 
4.6%
t299
 
4.4%
s273
 
4.0%
u227
 
3.3%
Other values (52)2512
36.6%
Distinct622
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:30.230454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length34
Mean length20.040794
Min length3

Characters and Unicode

Total characters18177
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique454 ?
Unique (%)50.1%

Sample

1st rowHarvard University
2nd rowImperial College London
3rd rowUniversity of Toronto
4th rowUniversity of Melbourne
5th rowTechnical University of Munich
ValueCountFrequency (%)
university688
28.5%
of389
 
16.1%
universidad34
 
1.4%
national25
 
1.0%
de23
 
1.0%
technology17
 
0.7%
state15
 
0.6%
tu15
 
0.6%
college12
 
0.5%
southern10
 
0.4%
Other values (699)1189
49.2%
2025-10-02T10:58:30.548453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i1998
 
11.0%
1510
 
8.3%
e1501
 
8.3%
n1413
 
7.8%
r1184
 
6.5%
t1104
 
6.1%
s1057
 
5.8%
o964
 
5.3%
a885
 
4.9%
U838
 
4.6%
Other values (57)5723
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1998
 
11.0%
1510
 
8.3%
e1501
 
8.3%
n1413
 
7.8%
r1184
 
6.5%
t1104
 
6.1%
s1057
 
5.8%
o964
 
5.3%
a885
 
4.9%
U838
 
4.6%
Other values (57)5723
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1998
 
11.0%
1510
 
8.3%
e1501
 
8.3%
n1413
 
7.8%
r1184
 
6.5%
t1104
 
6.1%
s1057
 
5.8%
o964
 
5.3%
a885
 
4.9%
U838
 
4.6%
Other values (57)5723
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1998
 
11.0%
1510
 
8.3%
e1501
 
8.3%
n1413
 
7.8%
r1184
 
6.5%
t1104
 
6.1%
s1057
 
5.8%
o964
 
5.3%
a885
 
4.9%
U838
 
4.6%
Other values (57)5723
31.5%

Program
Text

Distinct92
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:30.677963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length25
Median length23
Mean length16.637266
Min length6

Characters and Unicode

Total characters15090
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)5.8%

Sample

1st rowComputer Science
2nd rowData Science
3rd rowBusiness Analytics
4th rowEngineering
5th rowMechanical Engineering
ValueCountFrequency (%)
science411
23.6%
computer388
22.3%
engineering184
10.6%
data153
 
8.8%
software83
 
4.8%
information68
 
3.9%
analytics55
 
3.2%
artificial53
 
3.0%
intelligence53
 
3.0%
systems53
 
3.0%
Other values (60)240
13.8%
2025-10-02T10:58:30.902245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e2094
13.9%
n1435
 
9.5%
i1303
 
8.6%
c1137
 
7.5%
t1058
 
7.0%
r843
 
5.6%
834
 
5.5%
o804
 
5.3%
a679
 
4.5%
m583
 
3.9%
Other values (30)4320
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)15090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e2094
13.9%
n1435
 
9.5%
i1303
 
8.6%
c1137
 
7.5%
t1058
 
7.0%
r843
 
5.6%
834
 
5.5%
o804
 
5.3%
a679
 
4.5%
m583
 
3.9%
Other values (30)4320
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e2094
13.9%
n1435
 
9.5%
i1303
 
8.6%
c1137
 
7.5%
t1058
 
7.0%
r843
 
5.6%
834
 
5.5%
o804
 
5.3%
a679
 
4.5%
m583
 
3.9%
Other values (30)4320
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e2094
13.9%
n1435
 
9.5%
i1303
 
8.6%
c1137
 
7.5%
t1058
 
7.0%
r843
 
5.6%
834
 
5.5%
o804
 
5.3%
a679
 
4.5%
m583
 
3.9%
Other values (30)4320
28.6%

Level
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
Master
451 
Bachelor
297 
PhD
159 

Length

Max length8
Median length6
Mean length6.1289967
Min length3

Characters and Unicode

Total characters5559
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaster
2nd rowMaster
3rd rowMaster
4th rowMaster
5th rowMaster

Common Values

ValueCountFrequency (%)
Master451
49.7%
Bachelor297
32.7%
PhD159
 
17.5%

Length

2025-10-02T10:58:30.973745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-02T10:58:31.026039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
master451
49.7%
bachelor297
32.7%
phd159
 
17.5%

Most occurring characters

ValueCountFrequency (%)
a748
13.5%
e748
13.5%
r748
13.5%
h456
8.2%
M451
8.1%
t451
8.1%
s451
8.1%
B297
 
5.3%
c297
 
5.3%
l297
 
5.3%
Other values (3)615
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a748
13.5%
e748
13.5%
r748
13.5%
h456
8.2%
M451
8.1%
t451
8.1%
s451
8.1%
B297
 
5.3%
c297
 
5.3%
l297
 
5.3%
Other values (3)615
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a748
13.5%
e748
13.5%
r748
13.5%
h456
8.2%
M451
8.1%
t451
8.1%
s451
8.1%
B297
 
5.3%
c297
 
5.3%
l297
 
5.3%
Other values (3)615
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a748
13.5%
e748
13.5%
r748
13.5%
h456
8.2%
M451
8.1%
t451
8.1%
s451
8.1%
B297
 
5.3%
c297
 
5.3%
l297
 
5.3%
Other values (3)615
11.1%

Duration_Years
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8368247
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:31.080202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.94544922
Coefficient of variation (CV)0.33327728
Kurtosis-1.2433907
Mean2.8368247
Median Absolute Deviation (MAD)1
Skewness0.33722348
Sum2573
Variance0.89387423
MonotonicityNot monotonic
2025-10-02T10:58:31.145327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2423
46.6%
4276
30.4%
3171
18.9%
517
 
1.9%
116
 
1.8%
2.53
 
0.3%
1.51
 
0.1%
ValueCountFrequency (%)
116
 
1.8%
1.51
 
0.1%
2423
46.6%
2.53
 
0.3%
3171
18.9%
4276
30.4%
517
 
1.9%
ValueCountFrequency (%)
517
 
1.9%
4276
30.4%
3171
18.9%
2.53
 
0.3%
2423
46.6%
1.51
 
0.1%
116
 
1.8%

Tuition_USD
Real number (ℝ)

High correlation  Zeros 

Distinct274
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16705.017
Minimum0
Maximum62000
Zeros103
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:31.240186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12850
median7500
Q331100
95-th percentile48000
Maximum62000
Range62000
Interquartile range (IQR)28250

Descriptive statistics

Standard deviation16582.385
Coefficient of variation (CV)0.99265902
Kurtosis-0.79538039
Mean16705.017
Median Absolute Deviation (MAD)7500
Skewness0.71268849
Sum15151450
Variance2.749755 × 108
MonotonicityNot monotonic
2025-10-02T10:58:31.343589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0103
 
11.4%
350021
 
2.3%
150020
 
2.2%
420015
 
1.7%
380014
 
1.5%
3500013
 
1.4%
450012
 
1.3%
3120010
 
1.1%
400010
 
1.1%
320010
 
1.1%
Other values (264)679
74.9%
ValueCountFrequency (%)
0103
11.4%
4007
 
0.8%
4503
 
0.3%
5009
 
1.0%
8002
 
0.2%
9002
 
0.2%
10003
 
0.3%
11002
 
0.2%
12004
 
0.4%
13003
 
0.3%
ValueCountFrequency (%)
620002
 
0.2%
580004
0.4%
570001
 
0.1%
560003
0.3%
558001
 
0.1%
554001
 
0.1%
550001
 
0.1%
545001
 
0.1%
542001
 
0.1%
540005
0.6%

Living_Cost_Index
Real number (ℝ)

High correlation 

Distinct225
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.437486
Minimum27.8
Maximum122.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:31.445752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27.8
5-th percentile39.8
Q156.3
median67.5
Q372.2
95-th percentile83.8
Maximum122.4
Range94.6
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation14.056333
Coefficient of variation (CV)0.21813906
Kurtosis0.73565883
Mean64.437486
Median Absolute Deviation (MAD)6
Skewness-0.1066651
Sum58444.8
Variance197.58049
MonotonicityNot monotonic
2025-10-02T10:58:31.543875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.228
 
3.1%
68.928
 
3.1%
69.823
 
2.5%
65.422
 
2.4%
68.520
 
2.2%
67.818
 
2.0%
65.818
 
2.0%
64.516
 
1.8%
83.214
 
1.5%
70.513
 
1.4%
Other values (215)707
77.9%
ValueCountFrequency (%)
27.81
0.1%
28.51
0.1%
29.82
0.2%
30.52
0.2%
31.21
0.1%
31.82
0.2%
32.51
0.1%
33.22
0.2%
34.51
0.1%
34.81
0.1%
ValueCountFrequency (%)
122.41
 
0.1%
119.81
 
0.1%
116.51
 
0.1%
114.31
 
0.1%
112.11
 
0.1%
110.41
 
0.1%
108.91
 
0.1%
107.81
 
0.1%
1003
0.3%
95.23
0.3%

Rent_USD
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean969.20617
Minimum150
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:31.640640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile260
Q1545
median900
Q31300
95-th percentile1900
Maximum2500
Range2350
Interquartile range (IQR)755

Descriptive statistics

Standard deviation517.15475
Coefficient of variation (CV)0.5335859
Kurtosis-0.32234527
Mean969.20617
Median Absolute Deviation (MAD)400
Skewness0.53315801
Sum879070
Variance267449.04
MonotonicityNot monotonic
2025-10-02T10:58:31.936970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110054
 
6.0%
90051
 
5.6%
120047
 
5.2%
100046
 
5.1%
80041
 
4.5%
95040
 
4.4%
150039
 
4.3%
140036
 
4.0%
130036
 
4.0%
40031
 
3.4%
Other values (57)486
53.6%
ValueCountFrequency (%)
1501
 
0.1%
1604
 
0.4%
1703
 
0.3%
1806
0.7%
2007
0.8%
2204
 
0.4%
2302
 
0.2%
2405
 
0.6%
25013
1.4%
2606
0.7%
ValueCountFrequency (%)
25003
 
0.3%
24003
 
0.3%
23005
 
0.6%
22008
 
0.9%
21008
 
0.9%
200015
1.7%
190018
2.0%
18501
 
0.1%
180023
2.5%
170021
2.3%

Visa_Fee_USD
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.39691
Minimum40
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:32.024500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile80
Q1100
median160
Q3240
95-th percentile490
Maximum490
Range450
Interquartile range (IQR)140

Descriptive statistics

Standard deviation143.43574
Coefficient of variation (CV)0.67851388
Kurtosis-0.55259212
Mean211.39691
Median Absolute Deviation (MAD)70
Skewness1.0000705
Sum191737
Variance20573.811
MonotonicityNot monotonic
2025-10-02T10:58:32.107401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
160103
11.4%
45096
 
10.6%
23576
 
8.4%
9068
 
7.5%
12065
 
7.2%
10057
 
6.3%
8053
 
5.8%
49050
 
5.5%
48543
 
4.7%
14042
 
4.6%
Other values (20)254
28.0%
ValueCountFrequency (%)
408
 
0.9%
606
 
0.7%
706
 
0.7%
7521
 
2.3%
8053
5.8%
8812
 
1.3%
9068
7.5%
9927
 
3.0%
10057
6.3%
11036
4.0%
ValueCountFrequency (%)
49050
5.5%
48543
4.7%
45096
10.6%
35010
 
1.1%
3308
 
0.9%
27510
 
1.1%
2708
 
0.9%
2501
 
0.1%
2451
 
0.1%
23576
8.4%

Insurance_USD
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean700.07718
Minimum200
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:32.183492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile259
Q1450
median650
Q3800
95-th percentile1500
Maximum1500
Range1300
Interquartile range (IQR)350

Descriptive statistics

Standard deviation320.37487
Coefficient of variation (CV)0.45762794
Kurtosis1.0388359
Mean700.07718
Median Absolute Deviation (MAD)150
Skewness1.01755
Sum634970
Variance102640.06
MonotonicityNot monotonic
2025-10-02T10:58:32.259560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
800167
18.4%
650110
12.1%
60085
9.4%
150078
8.6%
40078
8.6%
75061
 
6.7%
30045
 
5.0%
90042
 
4.6%
70031
 
3.4%
35028
 
3.1%
Other values (10)182
20.1%
ValueCountFrequency (%)
20022
 
2.4%
25024
 
2.6%
28010
 
1.1%
30045
5.0%
3205
 
0.6%
35028
 
3.1%
40078
8.6%
45018
 
2.0%
50024
 
2.6%
55014
 
1.5%
ValueCountFrequency (%)
150078
8.6%
120020
 
2.2%
90042
 
4.6%
85024
 
2.6%
800167
18.4%
75061
 
6.7%
72021
 
2.3%
70031
 
3.4%
650110
12.1%
60085
9.4%

Exchange_Rate
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean623.00069
Minimum0.15
Maximum42150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2025-10-02T10:58:32.356812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.79
Q10.92
median1.35
Q37.15
95-th percentile1300
Maximum42150
Range42149.85
Interquartile range (IQR)6.23

Descriptive statistics

Standard deviation3801.7461
Coefficient of variation (CV)6.1023144
Kurtosis81.570863
Mean623.00069
Median Absolute Deviation (MAD)0.43
Skewness8.4824754
Sum565061.63
Variance14453274
MonotonicityNot monotonic
2025-10-02T10:58:32.456017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.92198
21.8%
193
 
10.3%
0.7993
 
10.3%
1.5286
 
9.5%
1.3582
 
9.0%
10.4521
 
2.3%
0.8920
 
2.2%
130020
 
2.2%
3.7516
 
1.8%
4.6516
 
1.8%
Other values (55)262
28.9%
ValueCountFrequency (%)
0.153
 
0.3%
0.315
 
0.6%
0.385
 
0.6%
0.7993
10.3%
0.8920
 
2.2%
0.92198
21.8%
193
10.3%
1.3412
 
1.3%
1.3582
9.0%
1.5286
9.5%
ValueCountFrequency (%)
421505
 
0.6%
244501
 
0.1%
156401
 
0.1%
156007
 
0.8%
150005
 
0.6%
123005
 
0.6%
39506
 
0.7%
1320.53
 
0.3%
130020
2.2%
860.21
 
0.1%

Interactions

2025-10-02T10:58:28.427142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:17.647375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.924171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:20.130642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:23.013464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.352680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.873956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.505811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:17.842397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.119747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:20.305941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:23.772835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.431406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.959167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.580422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.037390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.282559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:20.474204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:24.488694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.508642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.038440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.650225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.214539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.446882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:20.626017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:25.184347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.581370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.114931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.721222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.388269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.611482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:20.795104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:25.842881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.650788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.190793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.798594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.565225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.779440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:21.492425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:26.525149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.720621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.275350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.874928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:18.749856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:19.956091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:22.269682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.282096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:27.802667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-02T10:58:28.351608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-02T10:58:32.526506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Duration_YearsExchange_RateInsurance_USDLevelLiving_Cost_IndexRent_USDTuition_USDVisa_Fee_USD
Duration_Years1.0000.045-0.0170.789-0.0580.0320.1280.082
Exchange_Rate0.0451.000-0.5290.000-0.453-0.462-0.202-0.234
Insurance_USD-0.017-0.5291.0000.0390.8020.7340.3710.321
Level0.7890.0000.0391.0000.0390.0870.1900.136
Living_Cost_Index-0.058-0.4530.8020.0391.0000.8320.3120.262
Rent_USD0.032-0.4620.7340.0870.8321.0000.6000.476
Tuition_USD0.128-0.2020.3710.1900.3120.6001.0000.558
Visa_Fee_USD0.082-0.2340.3210.1360.2620.4760.5581.000

Missing values

2025-10-02T10:58:28.984363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-02T10:58:29.077811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryCityUniversityProgramLevelDuration_YearsTuition_USDLiving_Cost_IndexRent_USDVisa_Fee_USDInsurance_USDExchange_Rate
0USACambridgeHarvard UniversityComputer ScienceMaster2.05540083.5220016015001.00
1UKLondonImperial College LondonData ScienceMaster1.04120075.818004858000.79
2CanadaTorontoUniversity of TorontoBusiness AnalyticsMaster2.03850072.516002359001.35
3AustraliaMelbourneUniversity of MelbourneEngineeringMaster2.04200071.214004506501.52
4GermanyMunichTechnical University of MunichMechanical EngineeringMaster2.050070.51100755500.92
5JapanTokyoUniversity of TokyoInformation ScienceMaster2.0890076.41300220750145.80
6NetherlandsAmsterdamUniversity of AmsterdamArtificial IntelligenceMaster1.01580073.215001807200.92
7SingaporeSingaporeNational University of SingaporeFinanceMaster1.53500081.11900908001.34
8FranceParisSorbonne UniversityInternational RelationsMaster2.0450074.61400996500.92
9SwitzerlandZurichETH ZurichPhysicsMaster2.0146091.521008812000.89
CountryCityUniversityProgramLevelDuration_YearsTuition_USDLiving_Cost_IndexRent_USDVisa_Fee_USDInsurance_USDExchange_Rate
897Saudi ArabiaMakkahUmm Al-Qura UniversityInformation TechnologyPhD4.0430065.86502008003.75
898USASan FranciscoStanford UniversityData ScienceMaster2.05500095.2240016015001.00
899UKLeedsUniversity of LeedsComputer EngineeringMaster2.03500063.29004858000.79
900SpainZaragozaUniversity of ZaragozaArtificial IntelligencePhD4.0290063.2650807500.92
901ItalyPaduaUniversity of PaduaSoftware EngineeringMaster2.0340069.59001207000.92
902FranceStrasbourgUniversity of StrasbourgData AnalyticsMaster2.0400070.21000998500.92
903MalaysiaNilaiUSIMComputer ScienceBachelor3.0680050.54001204004.65
904Saudi ArabiaAl-AhsaKing Faisal UniversityInformation SystemsMaster2.0420064.26002008003.75
905USASeattleUniversity of WashingtonSoftware DevelopmentPhD5.05000077.8200016015001.00
906UKNottinghamUniversity of NottinghamData EngineeringMaster2.03400061.28004858000.79